The reduction of emissions of unpleasant odour generated by treatment plants is one of the most crucial aspects in air quality monitoring. In this context, the estimation of odour concentrations from a multiparameter sensors system is a very complex procedure and in the literature most of the studies have applied multilinear regression methods to assess odour concentration. In this paper, an Artificial Neural Network (ANN) has been performed to estimate the odour concentrations from emission sources and identify an estimation function of the odour concentrations, starting from data collected by a multi-parametric set of 10 sensors of gaseous compounds.
Artificial neural network in predicting odour concentrations: a case study
Veronica Distefano;
2023-01-01
Abstract
The reduction of emissions of unpleasant odour generated by treatment plants is one of the most crucial aspects in air quality monitoring. In this context, the estimation of odour concentrations from a multiparameter sensors system is a very complex procedure and in the literature most of the studies have applied multilinear regression methods to assess odour concentration. In this paper, an Artificial Neural Network (ANN) has been performed to estimate the odour concentrations from emission sources and identify an estimation function of the odour concentrations, starting from data collected by a multi-parametric set of 10 sensors of gaseous compounds.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
